Genetic Algorithm Model for Stock Management and Control

Genetic Algorithm Model for Stock Management and Control

Gabriel Babatunde Iwasokun, Shakirat Adeola Alimi
Copyright: © 2022 |Pages: 20
DOI: 10.4018/IJSDS.309119
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Abstract

Stock or inventory control and management have continued to face challenges that include inconsistent tracking, labor-intensive warehousing, inaccurate data, daunting manual documentation, and supply chain complexity. Research-based attempts to solve these challenges have continued to suffer one limitation or another. A genetic algorithm model for inventory control and management that addresses some of the limitations is presented in this paper. The model analyzes numerous orders whose chromosome generation and confirmation require previous order sets and takes the stock levels for the existing delivering sequence for the various products. The notable result of the implementation of the model is its attainment of a seamless, time-proven, high-accuracy, complex-computation-free, and cost-friendly platform for a reliable, functional, and result-oriented inventory system. It also established the relevance of genetic algorithms for achieving an on-demand and cognitive assessment of genetic variables against the selective and variable-compliant approach of the existing systems.
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Introduction

Inventory is the goods held for sale in the course of business. It is also the goods being held for manufacturing or selling purposes. It is a very important and vital component of a company and represents a significant source of future profit. It is composed of the raw materials, consumable items, components and spares, semi-processed materials, fuel and lubricants, finished goods, and other items that are essential to be stocked for the smooth running of the organization (Rakesh, 2016; Alebachew and Roy, 2002; Mohan, 2011). The key role of inventory is to guarantee supply and maintain operational continuity (Lambert, 2008). Inventory is classified into cycle stock, in-transit stock, safety or buffers stock, speculation stock, seasonal stock, and dead stock (Bloomberg et al. 2002). Inventories are kept for several reasons including economies of scale, balancing supply and demand, specialization protection from uncertainties, and season demand (Dhoka and Choudary, 2013). Existing inventory techniques include ABC Analysis, Just-in-Time (JIT), Material Requirement Planning (MRP), Economic Order Quality (EOQ), and Minimum Safety Stock (MSS). ABC analysis classifies the most important items (the highest price) into class A, those of intermediate importance (middle price) are classified as class B while the rest (low price) are classified into class C. The JIT method is based on the premise that the firm keeps a minimum level of inventory on hand, hoping for a timely delivery from the supplier. This method helps in ensuring continuity in the line of production. The MRP method is a set of procedures for converting forecast demand for a manufactured product into a schedule for obtaining components, sub-assemblies, and raw materials and it is useful for preventing unbalanced lot sizing. EOQ Model is the basic model for inventory control in production companies and it is based on unrealistic assumptions in which the demand is assumed to be constant. This form of assumption subjects EOQ to unattractiveness in most industrial settings. The MSS method is used to ascertain the safety level in inventory because a smaller safety level implies a greater risk of stock-outs (Obiri et al., 2015).

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